Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a partition component that receives; an entity matching component that selects in claim 17.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 17-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being incomplete for omitting essential structural cooperative relationships of elements, such omission amounting to a gap between the necessary structural connections. See MPEP § 2172.01. The omitted structural cooperative relationships are: upon review of the disclosure, it appears that there is no corresponding hardware in the claim or the spec for the components (see above).
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: claims 1-20 are directed to either a process, machine, manufacture or composition of matter.
With respect to claims 1, 9, 17:
2A Prong 1:
comparing a first record of the plurality of records to a second record of the plurality of records to generate a match result indicative of whether the first and second records describe a same item (encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data);
if the match result indicates that the first and second records do not describe the same item, initiating a search using at least a portion of at least one of the first and second records; (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data and perform searches);
each record including an item identifier that identifies an item that is described by the record and an attribute that relates to the item further define mental process.
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
claims 1, 9, obtaining a plurality of records from different data sources; receiving a search result; adding the search result to the plurality of records; claim 17 receives an input record (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
Claim 9, computing system, processor, memory (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358).
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
claims 1, 9, obtaining a plurality of records from different data sources; receiving a search result; adding the search result to the plurality of records; claim 17 receives an input record (mere data gathering and output recited at a high level of generality - insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)).
Claim 9, computing system, processor, memory (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358).
Further, the receiving/adding steps were considered to be extra-solution activity in Step 2A Prong 2, and thus it is re-evaluated in Step 2B to determine if it is more than what is well-understood, routine, conventional activity in the field. The receiving and/or transmitting limitations constitute extra-solution activity. See buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) ("That a computer receives and sends the information over a network-with no further specification-is not even arguably inventive."). The court decisions cited in MPEP 2106.05(d)(II) indicate that merely Receiving and/or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information). Thereby, a conclusion that the claimed receiving/transmitting steps are well-understood, routine, conventional activity is supported under Berkheimer. The claim is not patent eligible.
2, 10. The method of claim 1, wherein comparing further comprises: accessing a set of previously learned matches (data gathering); and determining whether the set of previously learned matches includes a match result for the first and second records corresponding to normalized forms of the first and second records (Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine data).
3, 11. The method of claim 2, wherein the set of previously learned matches are learned by a supervised machine learning system (additional element considered to be generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)).
4, 12. The method of claim 2, and further comprising: if the match result indicates that the plurality of different records do not describe the same item, then launching a web search using at least a part of at least one of the normalized forms; receiving search results; adding at least some of the search results to the input record set (data gathering); and normalizing and comparing the at least some search results added to the input record set (normalizing is mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
5, 13. The method of claim 2, wherein comparing comprises: identifying a similarity of attributes in the normalized forms corresponding to the first and second records (encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data); generating a similarity vector having vector values corresponding to the attributes, the vector values being indicative of the similarity of the corresponding attributes; generating a similarity measure based on the vector values; and generating the match result based on the similarity measure(normalizing and generating vectors are mental process – user can manually perform raw thinking in their head as a first stage and then using paper and pen to perform mathematical operation).
6, 14. The method of claim 1, wherein obtaining (data gathering) the plurality of records comprises: obtaining the plurality of records from different subsystems in a business system(data gathering).
7, 15, 19. The method of claim 1, and further comprising: partitioning an input record set into blocks based on partitioning criteria(Abstract idea of analyzing data. Mental process. A human- mind with pen and paper can generate/determine where to divide data).
8, 16, 20. The method of claim 7, wherein partitioning comprises: partitioning the input record set into blocks based on geographic location information contained in each record in the input record set (further define mental process).
18. The entity resolution system of claim 17, wherein the first and second records contain attributes, and further comprising: a record update component (computer component is recited at a high level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component; the mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention." Alice, 134 S. Ct. at 2358) that updates (data gathering) an entity record with the attributes from the first and second records in response to the match result indicating that the first and second records resolve to the same entity (encompasses mental observations or evaluations, e.g., a computer programmer’s mental identification of data).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2, 4-10, 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Johns (US2013/0268526) in view of Conrad (WO 2009086311).
Johns teaches
1, 9. A method comprising:
obtaining a plurality of records from different data sources (“With the advent of the Internet, millions of documents are available through Internet search engines. An electronic document is a cohesive body of text that is electronically accessible (e.g. a patent document, a news article, a legal case, a medical journal article or a webpage), 0006;
“The preferred methodology for searching a collection of electronically stored documents involves: selecting at least one category of sources; selecting at least one source (i.e. a collection of documents) within at least one category of sources; utilizing search terms to search the at least one source; returning related documents from the at least one source based on the search terms; collecting any of the related documents into a collection; permitting at least one related document returned to be selected for a further search utilizing the at least one related document as the search criteria in a selected source to return additional related documents; and creating a URL with all of the collected related documents stored at a location referenced in the URL”, 0021;
“The step of collecting any of the related documents into a collection may involve identifying the related documents to be collected from each source. The step of collecting documents may be performed to collect additional related documents after any search.”, 0022), each record including an item identifier that identifies an item that is described by the record and an attribute that relates to the item (records comprise of fields, headers, trailers, protocol data units, etc. “computing process may also associate each token with particular sections of the document”, 0056, 0094);
comparing a first record of the plurality of records to a second record of the plurality of records to generate a match result indicative of whether the first and second records describe a same item (“similarity calculation determines for each other document in the dataset a numeric similarity score. The computing process to determine the similarity score involves, with the possible aid of the statistics calculated during the computing process, comparing each token's count in a designated document (or text) to its matching token's count in each other document in the dataset. For a given token, the magnitude of closeness of the two such token counts between two documents has a directly proportional contribution to the magnitude of the similarity score (i.e. the closer the token counts are for each token included in two compared documents, the more significant the contribution to improving the similarity score).”, 0062);
if the match result indicates that the first and second records do not describe the same item (Conrad), initiating a search using at least a portion of at least one of the first and second records (“By conducting such a similarity calculation for all documents in a dataset, the top N most similar documents or least similar documents to a designated document or text can then easily be obtained. A given similarity score is consistently comparable to any other similarity score in the dataset, but it may not be comparable to a similarity score calculated by passing the designated document through some other entirely different dataset. Because the process defines that a similarity score is calculated for every document in the dataset, that total set of similarity scores can be used to normalize each of those similarity scores to something comparable across datasets. Given extremely normalized similarity scores, a given designated document can yield a useful single set of similar documents derived from multiple datasets, by applying the condition that a given normalized similarity score is beyond some standard threshold”, 0066; and permitting the related document returned to be selected for a further search utilizing the text of the related document as the search terms/criteria in a selected source to return additional related documents may further involve: comparing each token's);
receiving a search result; and
adding the search result to the plurality of records (Johns: “collecting any of the related documents into a collection may involve identifying the related documents to be collected from each source. The step of collecting documents may also be performed to collect additional related documents after any search, including after a further search is performed utilizing the entire text of the at least one related document”, 0091; collecting any of the related documents into a collection; permitting at least one related document returned to be selected for a further search utilizing the at least one related document as the search criteria in a selected source to return additional related documents; and creating a URL with all of the collected related documents stored at a location referenced in the URL”, 0021;
“The step of collecting any of the related documents into a collection may involve identifying the related documents to be collected from each source. The step of collecting documents may be performed to collect additional related documents after any search.”, 0022).
Johns performs searches for similar records but it may be argued that Johns fails to particularly call for if the match result indicates that the first and second records do not describe the same item and details of what a record comprises e.g., an identifier and an attribute.
Conrad teaches if the match result indicates that the first and second records do not describe the same item (“The primary client of the MRD is a matching algorithm designed to compare documents to master records. The matching algorithm does this by issuing a blocking query with information gleaned from a document and receiving a candidate list of master records in return. If a match is not found in the candidate list, additional queries may be issued and further matching attempts made. The data available in a given document will determine what queries, and in what order they will be employed to generate candidate lists. In order to present a homogenous representation of PII data present in a document for the purposes of querying and matching, a standard data structure for a person-centric identification record (ident) is used. Depending on how many persons appear in a document, multiple idents may be derived from a single document.”, page 5) and adding also adding result to records (Gathering, Stopping and Gathering Criteria: Two thresholds are used during an exemplary matching process, Tmgh and T.sub.LOW, in conjunction with the available blocks. T.sub.LOW, the threshold used as membership criterion, controls how many matches are collected. When the stopping criterion described below is met, then all candidate matches whose confidence rating scores meet or exceed this threshold are gathered and the matching process benefits from the underlying detailed inspection of a SVM classifier. Tmg.sub.h, the threshold used as stopping criterion, controls how early the matching stops for a given person-centric identification record (a.k.a. "ident"). In a given block, when a confidence rating score meets or exceeds this threshold, no additional blocking functions are invoked and all matches in the current block and previous blocks whose confidence rating scores meet or exceed T.sub.LOW are collected. Feature Vector Hashing: A large percentage of the feature vector”, page 9; details of what a record comprises e.g., an identifier and an attribute (“The exemplary ERD (entity resolution database) resolution engine uses a master record database (MRD)110 to store personal information about persons (or "entities") for the purpose of resolving documents to people. Populated from a trusted source (such as TransUnion©, Experian© commercial data sources), the MRD contains approximately 300 million "master records" representing all entities known to the engine. FIG. Ia shows master record database 110 as having a master record or data structure having a generic entity element 120 and multiple personally identifiable information (PII) elements 130. In the case of the exemplary MRD, this information includes name, address, phone, social security number (SSN), date-of-birth (DOB), date-of-death (DOD), and gender. (Some embodiments may omit one or more of these elements or include other elements.) FIG. Ib shows a specific entity element 120A serving as the anchor for the multiple pieces of specific identification information 13OA for an entity. An entity can have multiple names (married name, maiden name, an a.k.a (also known as)), multiple addresses (current, previous), multiple phone numbers, and so on. In the exemplary embodiment, an entity has at least a name and an address to appear in the MRD; however, some embodiments may pose other requirements, such as name and social security number or telephone. PII elements are not shared between entities. There are varying levels of PII element population across the set of 300 million master records.”, page 2).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and it is obvious if not inherent that records comprise of identifiers and attributes for the purpose of identifying what the record represents. Searching based on various criteria is a design choice and searching based on not finding a similar record allows for an exhaustive search to make sure there are no other identical records that are unknown.
2, 10. The method of claim 1, wherein comparing further comprises: accessing a set of previously learned matches; and determining whether the set of previously learned matches includes a match result for the first and second records corresponding to normalized forms of the first and second records (dynamic constantly learning and adding similar documents to previous collection or database “By conducting such a similarity calculation for all documents in a dataset, the top N most similar documents or least similar documents to a designated document or text can then easily be obtained. A given similarity score is consistently comparable to any other similarity score in the dataset, but it may not be comparable to a similarity score calculated by passing the designated document through some other entirely different dataset. Because the process defines that a similarity score is calculated for every document in the dataset, that total set of similarity scores can be used to normalize each of those similarity scores to something comparable across datasets. Given extremely normalized similarity scores, a given designated document can yield a useful single set of similar documents derived from multiple datasets, by applying the condition that a given normalized similarity score is beyond some standard threshold. It is important to note that while a high similarity score may often be better based on the computing process, it can also be the case that a low or average score would produce the best match of similar or dissimilar documents in certain situations.”, 0066;
Normalize, “normalize the counts such that the length of the document is less relevant or not relevant. For example, ten occurrences of a word in a single page document could be normalized to be equivalent to fifty occurrences of the same word in a five page document.”, 0057, 0066-7, 0088).
4, 12. The method of claim 2, and further comprising: if the match result indicates that the plurality of different records do not describe the same item, then launching a web search (0006, 0009, 0073, 0085) using at least a part of at least one of the normalized forms; receiving search results; adding at least some of the search results to the input record set; and normalizing and comparing the at least some search results added to the input record set (Normalize, “normalize the counts such that the length of the document is less relevant or not relevant. For example, ten occurrences of a word in a single page document could be normalized to be equivalent to fifty occurrences of the same word in a five page document.”, 0057, 0066-7, 0088).
Johns performs searches for similar records but it may be argued that Johns fails to particularly call for if the match result indicates that the first and second records do not describe the same item and details of what a record comprises e.g., an identifier and an attribute.
Conrad teaches if the match result indicates that the first and second records do not describe the same item (“The primary client of the MRD is a matching algorithm designed to compare documents to master records. The matching algorithm does this by issuing a blocking query with information gleaned from a document and receiving a candidate list of master records in return. If a match is not found in the candidate list, additional queries may be issued and further matching attempts made. The data available in a given document will determine what queries, and in what order they will be employed to generate candidate lists. In order to present a homogenous representation of PII data present in a document for the purposes of querying and matching, a standard data structure for a person-centric identification record (ident) is used. Depending on how many persons appear in a document, multiple idents may be derived from a single document.”, page 5) and adding also adding result to records (Gathering, Stopping and Gathering Criteria: Two thresholds are used during an exemplary matching process, Tmgh and T.sub.LOW, in conjunction with the available blocks. T.sub.LOW, the threshold used as membership criterion, controls how many matches are collected. When the stopping criterion described below is met, then all candidate matches whose confidence rating scores meet or exceed this threshold are gathered and the matching process benefits from the underlying detailed inspection of a SVM classifier. Tmg.sub.h, the threshold used as stopping criterion, controls how early the matching stops for a given person-centric identification record (a.k.a. "ident"). In a given block, when a confidence rating score meets or exceeds this threshold, no additional blocking functions are invoked and all matches in the current block and previous blocks whose confidence rating scores meet or exceed T.sub.LOW are collected. Feature Vector Hashing: A large percentage of the feature vector”, page 9).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and searching based on various criteria is a design choice and searching based on not finding a similar record allows for an exhaustive search to make sure there are no other identical records that are unknown.
5, 13. The method of claim 2, wherein comparing comprises: identifying a similarity of attributes in the normalized forms corresponding to the first and second records(Normalize plurality of records, e.g., “normalize the counts such that the length of the document is less relevant or not relevant. For example, ten occurrences of a word in a single page document could be normalized to be equivalent to fifty occurrences of the same word in a five page document.”, 0057, 0066-7, 0088); generating a similarity vector having vector values corresponding to the attributes, the vector values being indicative of the similarity of the corresponding attributes; generating a similarity measure based on the vector values; and generating the match result based on the similarity measure.
Johns fails to particularly call for using vectors.
Conrad teaches using vectors (“Block 640 entails determining whether one or more of the candidate records matches one or more of the public record idents determined at block 620. In the exemplary embodiment, this entails generating a set of one or more feature vectors. In particular, for each "ident permutation~MRD record" pair that results from the blocking result sets, feature vectors are generated by sending the available paired features through a set of feature-specific similarity functions. The resulting feature vector consists of a set of roughly 15 numeric values between 0 and 1. Identically matching features, like last_name and first_name, receive a value of 1.0, while fuzzier matches like "378 Carriage Green Lane" and "3740 Glenridge Grain Blvd" receive scores within the middle of this range. Next, these candidate matches, represented by their feature vectors, are input into an SVM (support vector machine), pre-trained on significant numbers of human judged matches, including both positive and negative examples, for the machine's classification (match/non-match).”, page 8).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and using vectors allows for generating a representative of records so they can be easily compared and/or clustered.
6, 14. The method of claim 1, wherein obtaining the plurality of records comprises: obtaining the plurality of records from different subsystems in a business system (Nonfunctional descriptive material).
Johns fails to call for businesses.
Conrad teaches data records can be tied to businesses (“The ERD resolution engine is capable of being extended in a number of ways. In the exemplary embodiment, both records in the MRD and incoming Public Records are person-centric. Other embodiments, however, redeploy the engine to other types of entities, entities such as companies and organizations, or locations, for example. Another example of an extension is the internationalization of the system. Designed into the system is a country field and intl_postal_field which can facilitate processing of non-US-based records. Other types of SVM classifiers (for example, non-polynomial) or other types of machine learning techniques (for example, Bayesian classifiers, Logistic Regression techniques, etc.) could be substituted for the particular SVM configuration used with competitive results”).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and indicating the records are related to businesses amounts to nonfunctional descriptive material. It allows for data to be personal or business data but algorithm would remain the same.
7, 15. The method of claim 1, and further comprising: partitioning an input record set into blocks based on partitioning criteria (reads on inherent fields in records and using tokens, 0052-0061).
8, 16. The method of claim 7, wherein partitioning comprises: partitioning the input record set into blocks based on geographic location information contained in each record in the input record set (reads on inherent fields in records, locations of words, fields, tokens, 0052-0061; geographic data can be country code or phone number data).
Conrad teaches blocks of data and phone numbers (“The exemplary ERD (entity resolution database) resolution engine uses a master record database (MRD)110 to store personal information about persons (or "entities") for the purpose of resolving documents to people. Populated from a trusted source (such as TransUnion©, Experian© commercial data sources), the MRD contains approximately 300 million "master records" representing all entities known to the engine. FIG. Ia shows master record database 110 as having a master record or data structure having a generic entity element 120 and multiple personally identifiable information (PII) elements 130. In the case of the exemplary MRD, this information includes name, address, phone, social security number (SSN), date-of-birth (DOB), date-of-death (DOD), and gender. (Some embodiments may omit one or more of these elements or include other elements.) FIG. Ib shows a specific entity element 120A serving as the anchor for the multiple pieces of specific identification information 13OA for an entity. An entity can have multiple names (married name, maiden name, an a.k.a (also known as)), multiple addresses (current, previous), multiple phone numbers, and so on. In the exemplary embodiment, an entity has at least a name and an address to appear in the MRD; however, some embodiments may pose other requirements, such as name and social security number or telephone. PII elements are not shared between entities. There are varying levels of PII element population across the set of 300 million master records.”, page 2); blocking queries, (“The exemplary system extracts entity information from input public records and constructs one or more blocking queries against specific portions of the master records database to identify one or more sets of candidate records.”, abstract).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and indicating the records are blocked and/or partitioned into various fields. Doing so allows for records to compared based on tokens or blocks of data and/or vectors.
17. An entity resolution system comprising: a partition component that receives an input record set that includes records from a plurality of different data sources and partitions the input record set into blocks based on partition criteria, each record relating to an entity (reads on inherent fields in records and using tokens, 0052-0061); and
an entity matching component that selects first and second records from a block, and outputs a match result indicative of whether the first and second records resolve to a same entity, wherein the entity matching determines whether previously learned resolutions are found for the first and second records and (“similarity calculation determines for each other document in the dataset a numeric similarity score. The computing process to determine the similarity score involves, with the possible aid of the statistics calculated during the computing process, comparing each token's count in a designated document (or text) to its matching token's count in each other document in the dataset. For a given token, the magnitude of closeness of the two such token counts between two documents has a directly proportional contribution to the magnitude of the similarity score (i.e. the closer the token counts are for each token included in two compared documents, the more significant the contribution to improving the similarity score).”, 0062) if not, compares the first and second records to determine whether the first and second records meet a similarity threshold and (0062) , if not, uses at least a portion of one of the first and second records to generate a search and obtain a search result, the entity matching component adding the search result to the block for selection by the entity matching component (Johns: “collecting any of the related documents into a collection may involve identifying the related documents to be collected from each source. The step of collecting documents may also be performed to collect additional related documents after any search, including after a further search is performed utilizing the entire text of the at least one related document”, 0091).
Johns performs searches for similar records but it may be argued that Johns fails to particularly call for if the match result indicates that the first and second records do not resolve to the same item.
Conrad teaches if the match result indicates that the first and second records do not resolve to the same item (“The primary client of the MRD is a matching algorithm designed to compare documents to master records. The matching algorithm does this by issuing a blocking query with information gleaned from a document and receiving a candidate list of master records in return. If a match is not found in the candidate list, additional queries may be issued and further matching attempts made. The data available in a given document will determine what queries, and in what order they will be employed to generate candidate lists. In order to present a homogenous representation of PII data present in a document for the purposes of querying and matching, a standard data structure for a person-centric identification record (ident) is used. Depending on how many persons appear in a document, multiple idents may be derived from a single document.”, page 5) and adding also adding result to records (Gathering, Stopping and Gathering Criteria: Two thresholds are used during an exemplary matching process, Tmgh and T.sub.LOW, in conjunction with the available blocks. T.sub.LOW, the threshold used as membership criterion, controls how many matches are collected. When the stopping criterion described below is met, then all candidate matches whose confidence rating scores meet or exceed this threshold are gathered and the matching process benefits from the underlying detailed inspection of a SVM classifier. Tmg.sub.h, the threshold used as stopping criterion, controls how early the matching stops for a given person-centric identification record (a.k.a. "ident"). In a given block, when a confidence rating score meets or exceeds this threshold, no additional blocking functions are invoked and all matches in the current block and previous blocks whose confidence rating scores meet or exceed T.sub.LOW are collected. Feature Vector Hashing: A large percentage of the feature vector”, page 9).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and searching based on various criteria is a design choice and searching based on not finding a similar record allows for an exhaustive search to make sure there are no other identical records that are unknown.
18. The entity resolution system of claim 17, wherein the first and second records contain attributes, and further comprising: a record update component that updates an entity record with the attributes from the first and second records in response to the match result indicating that the first and second records resolve to the same entity (similar or exact records are located and added to database, Johns: “collecting any of the related documents into a collection may involve identifying the related documents to be collected from each source. The step of collecting documents may also be performed to collect additional related documents after any search, including after a further search is performed utilizing the entire text of the at least one related document”, 0091).
19. The entity resolution system of claim 17, wherein the partitioning component is configured to: receive an input record set and partition the input record set into blocks based on partitioning criteria (reads on inherent fields in records and using tokens, 0052-0061).
20. The entity resolution system of claim 19, wherein the partitioning component is configured to: partition the input record set into blocks based on geographic location information contained in each record in the input record set. (reads on inherent fields in records and using tokens, 0052-0061; geographic data can be country code or phone number data).
Conrad teaches blocks of data and phone numbers (“The exemplary ERD (entity resolution database) resolution engine uses a master record database (MRD)110 to store personal information about persons (or "entities") for the purpose of resolving documents to people. Populated from a trusted source (such as TransUnion©, Experian© commercial data sources), the MRD contains approximately 300 million "master records" representing all entities known to the engine. FIG. Ia shows master record database 110 as having a master record or data structure having a generic entity element 120 and multiple personally identifiable information (PII) elements 130. In the case of the exemplary MRD, this information includes name, address, phone, social security number (SSN), date-of-birth (DOB), date-of-death (DOD), and gender. (Some embodiments may omit one or more of these elements or include other elements.) FIG. Ib shows a specific entity element 120A serving as the anchor for the multiple pieces of specific identification information 13OA for an entity. An entity can have multiple names (married name, maiden name, an a.k.a (also known as)), multiple addresses (current, previous), multiple phone numbers, and so on. In the exemplary embodiment, an entity has at least a name and an address to appear in the MRD; however, some embodiments may pose other requirements, such as name and social security number or telephone. PII elements are not shared between entities. There are varying levels of PII element population across the set of 300 million master records.”, page 2); blocking queries, (“The exemplary system extracts entity information from input public records and constructs one or more blocking queries against specific portions of the master records database to identify one or more sets of candidate records.”, abstract).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and indicating the records are blocked and/or partitioned into various fields. Doing so allows for records to compared based on tokens or blocks of data and/or vectors.
Claim Rejections - 35 USC § 103
Claim(s) 3, 11 are rejected under 35 U.S.C. 103 as being unpatentable over the combination of Johns and Conrad above in view of Zhang (US 201001987756).
3, 11. The method of claim 2, wherein the set of previously learned matches are learned by a supervised machine learning system.
Zhang teaches supervised machine learning system (“The method 200 for matching records applies a supervised learning method, for example, additive logistic regression, to match records stored in two different data sources. An exemplary system for executing method 200 may include four components: record preprocessing, record comparison, record matching, and determining a matching function/classifier through a learning process from training data that is labeled in advance”, 0036).
It would have been obvious to combine the references before the effective filing date because they are in the same field of endeavor and adding supervised machine learning allows for e.g., humans to label unlabeled/unknown data.
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/DAVID R VINCENT/Primary Examiner, Art Unit 2123